A PERSON DETECTION AND MULTI-VIEW VIDEO TRACKING K MEANS CLUSTERING OF FACES
Keywords:
Image processing, Digital Image Processing, Analog Image Processing Two dimensional signals.Abstract
A generic methodology for the semi-automatic generation of reliable position annotations for evaluating
multi-camera people-trackers on large video data sets. Most of the annotation data are automatically computed, by
estimating a consensus tracking result from multiple existing trackers and people detectors and classifying it as either
reliable or not.A small subset of the data, composed of tracks with insufficient reliability, is verified by a human using a
simple binary decision task, a process faster than marking the correct person position. The proposed framework is generic
and can handle additional trackers. In this thesis studied the most commonly used face edge detection techniques of K means
clustering of faces. Higher-level edge detection techniques and appropriate programming tools only facilitate the process but
do not make it a simple task.
